﻿using System;
using System.Collections.Generic;
using System.Text;

namespace MLForgeSharp.Models.SupervisedLearningModels.EnsembleLearning.Boosting
{
    /// <summary>
    /// 
    /// </summary>
    public class LightGBMModel
    {
        private class TreeNode
        {
            public int SplitFeature { get; set; } // 分裂特征
            public double SplitValue { get; set; } // 分裂值
            public TreeNode LeftChild { get; set; } // 左子节点
            public TreeNode RightChild { get; set; } // 右子节点
            public double Value { get; set; } // 叶节点的值
        }

        private List<TreeNode> trees; // 决策树集合
        private double learningRate; // 学习率
        private int maxDepth; // 树的最大深度
        private int numTrees; // 树的数量
        private Random random;

        public LightGBMModel(double learningRate = 0.1, int maxDepth = 6, int numTrees = 100)
        {
            this.learningRate = learningRate;
            this.maxDepth = maxDepth;
            this.numTrees = numTrees;
            this.trees = new List<TreeNode>();
            this.random = new Random();
        }

        // 计算梯度（对数损失）
        private double[] ComputeGradients(double[] predictions, double[] targets)
        {
            double[] gradients = new double[predictions.Length];
            for (int i = 0; i < predictions.Length; i++)
            {
                gradients[i] = predictions[i] - targets[i];
            }
            return gradients;
        }

        // 构建决策树
        private TreeNode BuildTree(double[][] data, double[] gradients, int currentDepth)
        {
            if (currentDepth >= maxDepth || data.Length <= 1)
            {
                double leafValue = 0.0;
                foreach (double gradient in gradients)
                {
                    leafValue += gradient;
                }
                leafValue /= data.Length;
                return new TreeNode { Value = leafValue };
            }

            int bestFeatureIndex = random.Next(data[0].Length);
            double bestSplitValue = data[random.Next(data.Length)][bestFeatureIndex];
            double bestLoss = double.MaxValue;

            List<double[]> leftData = new List<double[]>();
            List<double[]> rightData = new List<double[]>();
            List<double> leftGradients = new List<double>();
            List<double> rightGradients = new List<double>();

            foreach (double[] row in data)
            {
                if (row[bestFeatureIndex] < bestSplitValue)
                {
                    leftData.Add(row);
                    leftGradients.Add(gradients[Array.IndexOf(data, row)]);
                }
                else
                {
                    rightData.Add(row);
                    rightGradients.Add(gradients[Array.IndexOf(data, row)]);
                }
            }

            TreeNode node = new TreeNode
            {
                SplitFeature = bestFeatureIndex,
                SplitValue = bestSplitValue,
                LeftChild = BuildTree(leftData.ToArray(), leftGradients.ToArray(), currentDepth + 1),
                RightChild = BuildTree(rightData.ToArray(), rightGradients.ToArray(), currentDepth + 1)
            };

            return node;
        }

        // 训练模型
        public void Train(double[][] data, double[] targets)
        {
            double[] predictions = new double[targets.Length];
            for (int t = 0; t < numTrees; t++)
            {
                double[] gradients = ComputeGradients(predictions, targets);
                TreeNode tree = BuildTree(data, gradients, 0);
                trees.Add(tree);

                for (int i = 0; i < data.Length; i++)
                {
                    predictions[i] += learningRate * PredictTree(data[i], tree);
                }
            }
        }

        // 预测单棵树
        private double PredictTree(double[] features, TreeNode tree)
        {
            if (tree.Value != 0.0)
            {
                return tree.Value;
            }

            if (features[tree.SplitFeature] < tree.SplitValue)
            {
                return PredictTree(features, tree.LeftChild);
            }
            else
            {
                return PredictTree(features, tree.RightChild);
            }
        }

        // 预测
        public double Predict(double[] features)
        {
            double prediction = 0.0;
            foreach (TreeNode tree in trees)
            {
                prediction += learningRate * PredictTree(features, tree);
            }
            return prediction;
        }
    }

    // 示例程序
    public class LightGBMModelExample
    {
        public LightGBMModelExample()
        {
            // 示例数据
            double[][] data = new double[][]
        {
            new double[] { 1.0, 2.0 },
            new double[] { 1.1, 2.1 },
            new double[] { 1.2, 2.2 },
            new double[] { 1.3, 2.3 },
            new double[] { 10.0, 10.0 },
            new double[] { 10.1, 10.1 }
        };

            double[] targets = { 0.0, 0.0, 0.0, 0.0, 1.0, 1.0 }; // 目标值

            // 创建模型
            LightGBMModel model = new LightGBMModel(learningRate: 0.1, maxDepth: 6, numTrees: 100);

            // 训练模型
            model.Train(data, targets);

            // 预测
            double[] testFeatures = { 1.0, 2.0 };
            double predictedValue = model.Predict(testFeatures);

            System.Console.WriteLine("预测值: " + predictedValue);
        }
    }
}
